code: def _get_emformer_feature_extractor(
      input_dim: int, output_dim: int, use_bias: bool, stride: int
  ) -> FeatureEncoder:
      """Construct FeatureEncoder for emformer model.
  
      Args:
          input_dim (int): The feature dimension of log-mel spectrogram feature.
          output_dim (int): The feature dimension after linear layer.
          use_bias (bool): If ``True``, enable bias parameter in the linear layer.
          stride (int): Number of frames to merge for the output frame.
  
      Returns:
          FeatureEncoder: The resulting FeatureEncoder module.
      """
      return FeatureEncoder(input_dim, output_dim, use_bias, stride)
  
file_path: audio/src/torchaudio/prototype/models/_emformer_hubert.py
imports: from typing import List, Optional, Tuple
  import torch
  from torchaudio.models import Wav2Vec2Model
  from torchaudio.models.emformer import Emformer
  from torchaudio.models.rnnt import _TimeReduction
label_desc: Use image node_id 1 for calling a global function with example usage: _get_emformer_feature_extractor(input_dim, output_dim, use_bias, stride) and returns: FeatureEncoder